# 16-010/III (2016-02-15; 2017-01-23)

Author(s)
Chia-Lin Chang, National Chung Hsing University, Taichung, Taiwan; Tai-Lin Hsieh, National Chung Hsing University, Taichung, Taiwan; Michael McAleer, National Tsing Hua University, Taiwan; Erasmus University Rotterdam, The Netherlands; Complutense University of Madrid, Spain.
Keywords:
Stock market indexes, Exchange Traded Funds, Volatility Index (VIX), Vector autoregressions, moving average processes, conditional heteroskedasticity, diagonal BEKK
JEL codes:
C32, C58, G12, G15

As stock market indexes are not tradeable, the importance and trading volume of Exchange Traded Funds (ETFs) cannot be understated. ETFs track and attempt to replicate the performance of a specific index. Numerous studies have demonstrated a strong relationship between the S&P500 Composite Index and the Volatility Index (VIX), but few empirical studies have focused on the relationship between VIX and ETF returns. The purpose of the paper is to investigate whether VIX returns affect ETF returns by using vector autoregressive (VAR) models to determine whether daily VIX returns with different moving average processes affect ETF returns. The ARCH-LM test shows conditional heteroskedasticity in the estimation of ETF returns, so that the diagonal BEKK model is used to accommodate multivariate conditional heteroskedasticity in the VAR estimates of ETF returns. Daily data on ETF returns that follow different stock indexes in the USA and Europe are used in the empirical analysis. The estimates show that daily VIX returns have: (1) significant negative effects on European ETF returns in the short run; (2) stronger significant effects on single market ETF returns than on European ETF returns; and (3) lower impacts on the European ETF returns than on S&P500 returns.